Biosynthesis, evolution and ecology of microbial terpenoids.

Covering: through June 2021Terpenoids are the largest class of natural products recognised to date. While mostly known to humans as bioactive plant metabolites and part of essential oils, structurally diverse terpenoids are increasingly reported to be produced by microorganisms. For many of the compounds biological functions are yet unknown, but during the past years significant insights have been obtained for the role of terpenoids in microbial chemical ecology. Their functions include stress alleviation, maintenance of cell membrane integrity, photoprotection, attraction or repulsion of organisms, host growth promotion and defense. In this review we discuss the current knowledge of the biosynthesis and evolution of microbial terpenoids, and their ecological and biological roles in aquatic and terrestrial environments. Perspectives on their biotechnological applications, knowledge gaps and questions for future studies are discussed.

PlasmidEC and gplas2: an optimized short-read approach to predict and reconstruct antibiotic resistance plasmids in Escherichia coli.

Accurate reconstruction of Escherichia coli antibiotic resistance gene (ARG) plasmids from Illumina sequencing data has proven to be a challenge with current bioinformatic tools. In this work, we present an improved method to reconstruct E. coli plasmids using short reads. We developed plasmidEC, an ensemble classifier that identifies plasmid-derived contigs by combining the output of three different binary classification tools. We showed that plasmidEC is especially suited to classify contigs derived from ARG plasmids with a high recall of 0.941. Additionally, we optimized gplas, a graph-based tool that bins plasmid-predicted contigs into distinct plasmid predictions. Gplas2 is more effective at recovering plasmids with large sequencing coverage variations and can be combined with the output of any binary classifier. The combination of plasmidEC with gplas2 showed a high completeness (median=0.818) and F1-Score (median=0.812) when reconstructing ARG plasmids and exceeded the binning capacity of the reference-based method MOB-suite. In the absence of long-read data, our method offers an excellent alternative to reconstruct ARG plasmids in E. coli.